IWScore {TreeSearch}

Calculate the parsimony score of a tree given a dataset

Description

TreeLength() uses the Morphy library (Brazeau et al. 2017) to calculate a parsimony score for a tree, handling inapplicable data according to the algorithm of Brazeau et al. (2019). Trees may be scored using equal weights, implied weights (Goloboff 1993), or profile parsimony (Faith and Trueman 2001).

Usage

IWScore(tree, dataset, concavity = 10L, ...)

TreeLength(tree, dataset, concavity = Inf)

## S3 method for class 'phylo'
TreeLength(tree, dataset, concavity = Inf)

## S3 method for class 'numeric'
TreeLength(tree, dataset, concavity = Inf)

## S3 method for class 'list'
TreeLength(tree, dataset, concavity = Inf)

## S3 method for class 'multiPhylo'
TreeLength(tree, dataset, concavity = Inf)

Fitch(tree, dataset)

Arguments

tree

A tree of class phylo, a list thereof (optionally of class multiPhylo), or an integer – in which case tree random trees will be uniformly sampled.

dataset

A phylogenetic data matrix of phangorn class phyDat, whose names correspond to the labels of any accompanying tree.

concavity

Determines the degree to which extra steps beyond the first are penalized. Specify a numeric value to use implied weighting (Goloboff 1993); concavity specifies k in k / e + k. A value of 10 is recommended; TNT sets a default of 3, but this is too low in some circumstances (Goloboff et al. 2018; Smith 2019). Better still explore the sensitivity of results under a range of concavity values, e.g. k = 2 ^ (1:7). Specify Inf to weight each additional step equally. Specify "profile" to employ profile parsimony (Faith and Trueman 2001).

...

unused; allows additional parameters specified within ... to be received by the function without throwing an error.

Value

TreeLength() returns a numeric vector containing the score for each tree in tree.

Author(s)

Martin R. Smith (using Morphy C library, by Martin Brazeau)

References

Brazeau MD, Guillerme T, Smith MR (2019). “An algorithm for morphological phylogenetic analysis with inapplicable data.” Systematic Biology, 68(4), 619–631. doi:10.1093/sysbio/syy083.

Brazeau MD, Smith MR, Guillerme T (2017). “MorphyLib: a library for phylogenetic analysis of categorical trait data with inapplicability.” doi:10.5281/zenodo.815372.

Faith DP, Trueman JWH (2001). “Towards an inclusive philosophy for phylogenetic inference.” Systematic Biology, 50(3), 331–350. doi:10.1080/10635150118627.

Goloboff PA (1993). “Estimating character weights during tree search.” Cladistics, 9(1), 83–91. doi:10.1111/j.1096-0031.1993.tb00209.x.

Goloboff PA, Torres A, Arias JS (2018). “Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology.” Cladistics, 34(4), 407–437. doi:10.1111/cla.12205.

Smith MR (2019). “Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets.” Biology Letters, 15(2), 20180632. doi:10.1098/rsbl.2018.0632.

See Also

• Conduct tree search using MaximizeParsimony() (command line), EasyTrees() (graphical user interface), or TreeSearch() (custom optimality criteria).

• See score for each character: CharacterLength().

Other tree scoring: CharacterLength(), LengthAdded(), MinimumLength(), MorphyTreeLength(), TaxonInfluence()

Examples

data("inapplicable.datasets")
tree <- TreeTools::BalancedTree(inapplicable.phyData[[1]])
TreeLength(tree, inapplicable.phyData[[1]])
TreeLength(tree, inapplicable.phyData[[1]], concavity = 10)
TreeLength(tree, inapplicable.phyData[[1]], concavity = "profile")
TreeLength(5, inapplicable.phyData[[1]])